二代曲线波图像恢复模型及其算法

Abstract

Abstract:

Suppose that an image belongs to Besov spaces, one can measure sparse decompositions on wavelet basis by the fact that the Besov norms have equivalent descriptions by means of wavelet coefficients. But as is well known, wavelets fail to very efficiently represent smooth objects with edges while curvelets provide an optimally sparse representation of objects with singularities along C2 edges. Based on the analysis above, a novel model using second-generation curvelets is proposed for restoration of the image. Especially, curvelets-type decomposition spaces are employed for characterizing the sparsity of second-generation curvelet coefficients. On the other hand, an iterative hard shrinkage algorithm is obtained by using the generalized conditional gradient method, as well as convergent theorems for solutions and stopping criterion. Finally, experiments show that the proposed algorithm produces better results in terms of both signal-to-noise ratio and subjective visual quality than methods available.

Key words: second-generation curvelets, iteration, shrinkage, image restoration model, sparsity representation

CLC Number:

TN911

References

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Image restoration model and algorithms with second-generation curvelets

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